The Effect of Teaching English through Literature on Creative Writing at HSSC Level in Pakistan
Bibliographic record
Abstract
Creative writing means the ESL/EFL learners’ capacity to respond vividly and spontaneously, and to convey responses freely in their writing. This study brings to light the fact that teaching English through literature does not render any positive pay off in developing and honing the EFL/ESL learners’ creative writing. In the Pakistani context, literature seems to be inadequate and improper language teaching tool at HSSC level. To achieve the set objectives of this study, the researcher went for the quantitative research methodology. So, a questionnaire comprising of 15 items encompassing the different aspects of creative writing was designed to collect data from 600 subjects (male/female) of intermediate level. The researcher also conducted an achievement test so that a correlation might be drawn between their attitude towards “developing creative writing through literature” and the score of their achievement test. The collected data were analyzed through software package (SPSS XX). The findings of this study explicitly reveal that the EFL learners remain unable to develop both the language skills (particularly writing skill) and language areas when they are taught English through literature. This study recommends that the teaching of English should be application oriented and task-based strategies and activities should be resorted to by the EL educators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".